Category Archives: Uncategorized

Authors: Roshni Dhruv, Omi Navadiya, Reena Desai

Abstract: In the last few years, talk about IoT and 5G has left the boardrooms and landed in real places—factories, hospitals, city streets, even farms. This paper digs into that shift. We look at what actually happens when you put 5G’s speed and low latency together with IoT’s huge reach, and why that pairing matters much more than each alone. You’ll see real-world deployments, the tough problems people are running into, and a peek at what’s next—security issues that still keep folks up at night, plus some genuinely promising ideas with AI-powered networks and ambient sensing. We get into the details of enabling tech like edge computing, digital twins, and network slicing, right alongside new standards, economic outlooks, and the rules and regulations steering all of this. The point here isn’t to hype things up—it’s to spell out what’s actually going on, what’s working, what’s tricky, and why you should care.

DOI: https://doi.org/10.5281/zenodo.19703853

Published by: vikaspatanker

Authors: H. M. Pawar, Abhishek Kavthekar, Sanket Gaware, Kartik Chawan, Sunny Bhagawat

Abstract: Rapid urbanization and the continuous increase in vehicular traffic have made conventional traffic signal systems inefficient, leading to congestion, increased travel time, and higher fuel consumption. Traditional fixed-timing traffic lights fail to adapt to real-time traffic conditions, resulting in unnecessary delays even when certain lanes have minimal or no traffic.This project proposes a Sub-Smart Traffic Signal Control System, an intelligent yet cost-effective solution designed to optimize traffic flow using real-time data. The system utilizes sensors such as infrared (IR), ultrasonic, or camera-based modules to detect vehicle density on different lanes. Based on the collected data, the signal timing is dynamically adjusted, giving priority to lanes with higher traffic density while reducing idle time for less congested routes.Additionally, the system can be extended to include emergency vehicle detection, enabling automatic signal clearance for ambulances and fire services. The proposed solution aims to minimize traffic congestion, reduce fuel wastage, and improve overall road efficiency without the high infrastructure costs associated with fully smart traffic systems.The implementation demonstrates how a semi-automated (“sub-smart”) approach can significantly enhance traffic management in developing urban areas, making it a practical and scalable solution for modern cities.

Published by: vikaspatanker

Authors: Prakhar Kulshrestha, Tanmoy, Anoushka Das, Shubhradip

Abstract: This paper reviews the design and development of Ranger’s Bad Luck, an arcade-style 3D video game built using agile methodologies. The game incorporates real-time graphics, a physics engine, and immersive sound effects to enhance the player experience. This review evaluates the advantages, limitations, and technological choices made during development, while situating the project within the broader context of game design practices. The findings indicate that the integration of agile development methods and modern tools such as Unreal Engine and Blender enabled efficient prototyping and implementation, though limitations regarding performance and scalability remain.

Published by: vikaspatanker

Authors: Naveen Kumar K,, Bhargav Simha N, Mahendra Chowdary V, Dr.S.Vijayaragavan

Abstract: Lung cancer is one of the leading causes of cancer-related mortality worldwide, and early detection significantly improves patient survival rates. Traditional diagnostic methods such as CT-scan interpretation are time-consuming and require high clinical expertise. In this project, we propose an automated lung cancer detection system using an Attention-Enhanced Inception NeXt–based deep learning model. The model integrates the representational efficiency of the Inception NeXt architecture with an attention mechanism that highlights discriminative lung regions, enabling more accurate identification of cancerous nodules A pre-processed dataset of CT scan images is used to train and evaluate the model. Image augmentation, normalization, and lung-region enhancement techniques are applied to improve data quality and reduce overfitting. The proposed hybrid architecture demonstrates superior feature extraction capabilities and improved sensitivity compared to conventional CNNs. Experimental results indicate that the model achieves high accuracy, precision, recall, and F1-score, making it a reliable tool for assisting radiologists in early lung cancer diagnosis. This system has the potential to support faster, more consistent, and more accurate clinical decision-making.

DOI: https://zenodo.org/records/19699961

Published by: vikaspatanker

Authors: Mr. Omesh Wadhwani, Bhagyashri Rahangdale, Dhanashree Dahake, Parika Pandharkar, Rishita Pokhare

Abstract: This research paper presents a detailed exploration of an AI-based traffic management system leveraging the YOLOv8n object detection model. The system aims to improve traffic flow, reduce congestion, and enhance overall road safety through real-time analysis of traffic conditions. The paper covers various aspects, including the system architecture, the implementation details of YOLOv8n for vehicle detection and tracking, the integration of detected data into a traffic management platform, and the experimental results demonstrating the system's performance and effectiveness. The study also addresses challenges in deploying AI-based traffic management systems and suggests potential solutions for future research and development. The proposed system is trained using the COCO dataset along with custom traffic video data to ensure robustness under different environmental conditions. Performance evaluation is carried out using standard metrics such as precision, recall, and detection accuracy. Experimental results show that the model achieves a precision of 0.92, recall of 0.89, and overall detection accuracy of 91%, while effectively estimating traffic density in real-time scenarios. These results demonstrate the system’s capability to support adaptive signal timing and significantly improve traffic efficiency.

DOI: https://doi.org/10.5281/zenodo.19698223

Published by: vikaspatanker

Authors: Viraja kanawally

Abstract: Artificial intelligence is becoming increasingly integrated into recruitment and is changing the paradigm in human resource management practices by helping organizations become more efficient in their hiring, decreasing time to hire, and improving quality of hire performance. The current paper explores how predictive analytics driven by AI technology can be applied within a recruitment process by automating resume screening, job-candidate match, and employee turnover predictions. Using survey data collected from 304 firms based in Europe who have adopted AI tools for recruiting purposes, it is found that AI can cut down time to hire by 48.8%, reduce cost per hire by 54.6%, and increase retention rates by 17.9%. Still, 15% of organizations adopt AI to predict internal mobility. The major reasons preventing them from doing so are fears about algorithmic bias, excessive costs associated with AI tool adoption, and resistance from applicants. A framework for predicting recruitment outcomes with the help of AI will be presented.

DOI: https://doi.org/10.5281/zenodo.19697173

Published by: vikaspatanker

Authors: Anam Siddiqui, Saima Shaikh, Sana Shaikh, Alfiya Shaikh, Prof. Nargis Shaikh

Abstract: Personal safety has become a critical concern in modern society due to the increasing rate of crimes and emergency situations. This paper presents a Human Safety Device that integrates Internet of Things (IoT) technology with dual communication systems, namely GSM-based SMS alerts and Telegram-based real-time notifications, to ensure reliable emergency response. The system is built using an ESP32 microcontroller interfaced with a GPS module for location tracking, a pulse sensor for heart rate monitoring, and an MPU6050 sensor for motion detection. In emergency conditions, triggered manually via a panic button or automatically through abnormal sensor readings, the system captures and transmits location and health data to predefined contacts. Experimental evaluation shows that the system achieves an average alert response time of 3–5 seconds for Telegram notifications and 5–10 seconds for GSM-based SMS delivery. The GPS module provides location accuracy within ±5–10 meters, while sensor readings maintain an accuracy of approximately 95% under normal conditions. Additionally, a web-based interface enables real-time monitoring and visualization of user data. The proposed system is compact, cost-effective, and highly reliable, making it suitable for real-world deployment in personal safety applications.

Published by: vikaspatanker

Authors: Manasi Waghe, Danish Chandargi, Mohammad Aamir Rayyan, Raviraj Joshi, Dr. A.R. Deshpande

Abstract: The translation of government and legal documents from Marathi to English poses unique challenges due to linguis- tic complexity, domain-specific terminology, structural richness, and low-resource constraints. General-purpose machine translation systems often fail to maintain semantic fidelity, formatting, and terminological consistency required for administrative and legal texts. This survey explores recent advances in multilingual machine translation, domain adaptation techniques, OCR-driven document understanding, Marathi-specific NLP resources, and terminology- constrained translation methods. We examine the state-of-the-art in robust Marathi-to-English translation systems and highlight critical gaps, focusing on integrating layout-aware models and domain- specific constraints to improve translation quality and reliability for official government documentation.

Published by: vikaspatanker

Authors: Srinithi G D, Hidhesh R M

 

Abstract: Pneumonia remains one of the leading causes of mortality worldwide, particularly among children under five and the elderly. Early and accurate diagnosis through chest X-ray interpretation is critical, yet manual analysis by radiologists is time-consuming, subjective, and prone to inter-observer variability. This paper presents a deep learning-based approach for automated pneumonia detection from chest X-ray images using transfer learning with pre-trained convolutional neural network (CNN) architectures. We evaluate the performance of three widely adopted models — ResNet50, VGG16, and DenseNet121 — on the publicly available Kaggle Chest X-Ray Images (Pneumonia) dataset containing 5,856 labeled images. The models are fine-tuned with data augmentation techniques to improve generalization. Our experimental results demonstrate that DenseNet121 achieves the highest classification accuracy of 93.27%, with a recall of 97.44% for pneumonia-positive cases, outperforming both ResNet50 (91.83%) and VGG16 (90.06%). The proposed framework offers a reliable, efficient, and scalable computer-aided diagnostic (CAD) tool that can assist radiologists in clinical decision-making, particularly in resource-constrained healthcare settings.

DOI: https://doi.org/10.5281/zenodo.19942378

 

Published by: vikaspatanker

Authors: Dr. Pravin Kumar, Abhinav Kumar

Abstract: This study evaluates the effectiveness of Janani Suraksha Yojana (JSY) in improving maternal and child health outcomes in Bihar. Using a mixed-method approach and regression analysis, the study finds that awareness and education significantly influence institutional delivery, while barriers such as cost and accessibility persist

DOI: https://doi.org/10.5281/zenodo.19693897

Published by: vikaspatanker